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Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials
dc.contributor.author | Romero, Vanessa | |
dc.contributor.author | Rumí, Rafael | |
dc.contributor.author | Salmerón Cerdán, Antonio | |
dc.date.accessioned | 2012-05-28T09:50:29Z | |
dc.date.available | 2012-05-28T09:50:29Z | |
dc.date.issued | 2004 | |
dc.identifier.citation | Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM'04), pp. 177-184. | es_ES |
dc.identifier.uri | http://hdl.handle.net/10835/1556 | |
dc.description.abstract | In this paper we introduce a hill-climbing algorithm for structural learning of Bayesian networks from databases with discrete and continuous variables. The process is based on the optimisation of a metric that measures the accuracy of a network penalised by its complexity. The result of the algorithm is a network where the conditional distribution for each variable is a mixture of truncated exponentials (MTE), so that no restrictions on the network topology are imposed. The using artificial and real world data. | es_ES |
dc.language.iso | en | es_ES |
dc.source | Second European Workshop on Probabilistic Graphical Models (PGM'04) | es_ES |
dc.title | Structural Learning of Bayesian Networks with Mixtures of Truncated Exponentials | es_ES |
dc.type | info:eu-repo/semantics/report | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |